As genomic research becomes more complex and data-rich, artificial intelligence (AI) has
emerged as a crucial tool for processing and analyzing high-dimensional genomic data …

To enable personalized cancer treatment, machine learning models have been developed
to predict drug response as a function of tumor and drug features. However, most algorithm …

Background: DNA N6-methyladenine plays an important role in the restriction-modification
system to isolate invasion from adventive DNA. The shortcomings of the high time …

Motivation The emergence of drug-resistant bacteria makes the discovery of new antibiotics
an urgent issue, but finding new molecules with the desired antibacterial activity is an …

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their
heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks …

Widespread drug resistance has become the key issue in global healthcare. Extensive
efforts have been made to reveal not only diverse diseases experiencing drug resistance …

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear
neutrophils and an important biomarker of infectious diseases. The correct identification of …

Abstract Deep learning with Convolutional Neural Networks has shown great promise in
image-based classification and enhancement but is often unsuitable for predictive modeling …

Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …

Predicting the response of a cancer cell line to a therapeutic drug is an important topic in
modern oncology that can help personalized treatment for cancers. Although numerous …